Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations2568724
Missing cells14872
Missing cells (%)< 0.1%
Duplicate rows14914
Duplicate rows (%)0.6%
Total size in memory2.1 GiB
Average record size in memory861.6 B

Variable types

DateTime1
Numeric23
Text6
Categorical5

Alerts

Dataset has 14914 (0.6%) duplicate rowsDuplicates
GINI is highly overall correlated with ingreso_laboral_promedio and 1 other fieldsHigh correlation
IPUG is highly overall correlated with edad_promedio and 2 other fieldsHigh correlation
ajustada_alineacion is highly overall correlated with alineación con portafolio estratégico and 2 other fieldsHigh correlation
alineación con portafolio estratégico is highly overall correlated with ajustada_alineacion and 2 other fieldsHigh correlation
cantidad is highly overall correlated with log_cantidad and 2 other fieldsHigh correlation
cantidad_promedio is highly overall correlated with precio_promedio and 3 other fieldsHigh correlation
categoria is highly overall correlated with categoria_macroHigh correlation
categoria_macro is highly overall correlated with categoriaHigh correlation
edad_promedio is highly overall correlated with IPUG and 2 other fieldsHigh correlation
id is highly overall correlated with num_pedidos and 1 other fieldsHigh correlation
ingreso_laboral_promedio is highly overall correlated with GINI and 4 other fieldsHigh correlation
log_cantidad is highly overall correlated with cantidad and 2 other fieldsHigh correlation
log_precio is highly overall correlated with cantidad and 2 other fieldsHigh correlation
log_valor is highly overall correlated with ajustada_alineacion and 2 other fieldsHigh correlation
num_pedidos is highly overall correlated with id and 2 other fieldsHigh correlation
pedido is highly overall correlated with idHigh correlation
porcentaje_urbano is highly overall correlated with ingreso_laboral_promedio and 1 other fieldsHigh correlation
precio is highly overall correlated with cantidad and 2 other fieldsHigh correlation
precio_promedio is highly overall correlated with cantidad_promedio and 1 other fieldsHigh correlation
ticket_promedio is highly overall correlated with cantidad_promedio and 2 other fieldsHigh correlation
total_gasto is highly overall correlated with cantidad_promedio and 3 other fieldsHigh correlation
total_productos is highly overall correlated with cantidad_promedio and 4 other fieldsHigh correlation
valor is highly overall correlated with ajustada_alineacion and 2 other fieldsHigh correlation
zona is highly overall correlated with GINI and 4 other fieldsHigh correlation
cantidad is highly skewed (γ1 = 407.6718452) Skewed
precio is highly skewed (γ1 = 75.64533226) Skewed
valor is highly skewed (γ1 = 88.62689348) Skewed
alineación con portafolio estratégico is highly skewed (γ1 = -638.3338094) Skewed
ajustada_alineacion is highly skewed (γ1 = -164.0441718) Skewed
total_productos is highly skewed (γ1 = 23.56907809) Skewed
precio_promedio is highly skewed (γ1 = 24.84345157) Skewed
cantidad_promedio is highly skewed (γ1 = 73.01971405) Skewed
Total de edificaciones en obra has 678206 (26.4%) zeros Zeros

Reproduction

Analysis started2025-04-06 05:45:04.610792
Analysis finished2025-04-06 05:55:19.622739
Duration10 minutes and 15.01 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

fecha
Date

Distinct756
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 MiB
Minimum1971-01-02 00:00:00
Maximum1973-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-06T00:55:19.748473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:55:19.893044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pedido
Real number (ℝ)

High correlation 

Distinct933814
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466252.12
Minimum2
Maximum933936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:20.112540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile46643.15
Q1230870
median465819
Q3701243
95-th percentile887652
Maximum933936
Range933934
Interquartile range (IQR)470373

Descriptive statistics

Standard deviation270294.22
Coefficient of variation (CV)0.57971688
Kurtosis-1.2057948
Mean466252.12
Median Absolute Deviation (MAD)235227
Skewness0.0035068703
Sum1.197673 × 1012
Variance7.3058966 × 1010
MonotonicityNot monotonic
2025-04-06T00:55:20.327293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
589388 152
 
< 0.1%
431390 136
 
< 0.1%
47364 114
 
< 0.1%
679361 111
 
< 0.1%
918192 108
 
< 0.1%
17776 108
 
< 0.1%
488822 108
 
< 0.1%
918218 108
 
< 0.1%
607844 104
 
< 0.1%
573522 104
 
< 0.1%
Other values (933804) 2567571
> 99.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 2
 
< 0.1%
7 8
< 0.1%
8 2
 
< 0.1%
9 4
< 0.1%
10 1
 
< 0.1%
11 4
< 0.1%
ValueCountFrequency (%)
933936 2
< 0.1%
933935 1
 
< 0.1%
933934 1
 
< 0.1%
933933 3
< 0.1%
933932 1
 
< 0.1%
933931 1
 
< 0.1%
933930 1
 
< 0.1%
933929 2
< 0.1%
933928 2
< 0.1%
933927 4
< 0.1%

id
Real number (ℝ)

High correlation 

Distinct419222
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165617.2
Minimum1
Maximum419226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:20.567333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9362
Q158899
median138026
Q3261522
95-th percentile384302
Maximum419226
Range419225
Interquartile range (IQR)202623

Descriptive statistics

Standard deviation121181.67
Coefficient of variation (CV)0.73169737
Kurtosis-1.0227461
Mean165617.2
Median Absolute Deviation (MAD)94513
Skewness0.45437008
Sum4.2542488 × 1011
Variance1.4684998 × 1010
MonotonicityNot monotonic
2025-04-06T00:55:20.726968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3037 1735
 
0.1%
1236 1671
 
0.1%
2906 1555
 
0.1%
3121 1361
 
0.1%
3357 1318
 
0.1%
2412 1173
 
< 0.1%
30822 998
 
< 0.1%
4206 945
 
< 0.1%
28478 756
 
< 0.1%
512 644
 
< 0.1%
Other values (419212) 2556568
99.5%
ValueCountFrequency (%)
1 85
< 0.1%
2 18
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 20
 
< 0.1%
6 36
 
< 0.1%
7 20
 
< 0.1%
8 18
 
< 0.1%
9 92
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
419226 2
 
< 0.1%
419225 1
 
< 0.1%
419224 1
 
< 0.1%
419223 1
 
< 0.1%
419222 1
 
< 0.1%
419221 11
< 0.1%
419220 8
< 0.1%
419219 1
 
< 0.1%
419218 6
< 0.1%
419217 2
 
< 0.1%

edad
Real number (ℝ)

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.752713
Minimum18
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:20.861998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile29
Q131
median43
Q348
95-th percentile58
Maximum67
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.5305458
Coefficient of variation (CV)0.22826172
Kurtosis-0.91711203
Mean41.752713
Median Absolute Deviation (MAD)8
Skewness0.17876145
Sum1.072512 × 108
Variance90.831304
MonotonicityNot monotonic
2025-04-06T00:55:21.027713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 402001
 
15.6%
45 154275
 
6.0%
43 151451
 
5.9%
47 110215
 
4.3%
41 108391
 
4.2%
40 95676
 
3.7%
50 91581
 
3.6%
30 91125
 
3.5%
46 88733
 
3.5%
42 86804
 
3.4%
Other values (40) 1188472
46.3%
ValueCountFrequency (%)
18 11
 
< 0.1%
19 240
 
< 0.1%
20 234
 
< 0.1%
21 575
 
< 0.1%
22 4635
 
0.2%
23 9742
 
0.4%
24 6819
 
0.3%
25 10180
 
0.4%
26 17266
0.7%
27 25943
1.0%
ValueCountFrequency (%)
67 466
 
< 0.1%
66 22
 
< 0.1%
65 90
 
< 0.1%
64 21378
 
0.8%
63 2448
 
0.1%
62 7280
 
0.3%
61 5786
 
0.2%
60 33558
1.3%
59 31561
1.2%
58 57713
2.2%
Distinct807
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.9 MiB
2025-04-06T00:55:21.813738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length8.5339153
Min length3

Characters and Unicode

Total characters21921273
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowEL CARMEN DE CHUCURI
2nd rowVILLANUEVA
3rd rowVILLANUEVA
4th rowVILLANUEVA
5th rowVILLANUEVA
ValueCountFrequency (%)
curiti 575851
19.3%
villanueva 569096
19.1%
natagaima 344938
 
11.6%
guatica 130373
 
4.4%
girardota 76933
 
2.6%
de 73795
 
2.5%
santa 55005
 
1.8%
matias 41568
 
1.4%
don 41568
 
1.4%
la 41561
 
1.4%
Other values (808) 1032091
34.6%
2025-04-06T00:55:22.766724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4411816
20.1%
I 2769839
12.6%
U 1603758
 
7.3%
T 1514641
 
6.9%
L 1511166
 
6.9%
N 1433658
 
6.5%
R 1293458
 
5.9%
V 1237070
 
5.6%
E 1222356
 
5.6%
C 1085694
 
5.0%
Other values (25) 3837817
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21921273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4411816
20.1%
I 2769839
12.6%
U 1603758
 
7.3%
T 1514641
 
6.9%
L 1511166
 
6.9%
N 1433658
 
6.5%
R 1293458
 
5.9%
V 1237070
 
5.6%
E 1222356
 
5.6%
C 1085694
 
5.0%
Other values (25) 3837817
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21921273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4411816
20.1%
I 2769839
12.6%
U 1603758
 
7.3%
T 1514641
 
6.9%
L 1511166
 
6.9%
N 1433658
 
6.5%
R 1293458
 
5.9%
V 1237070
 
5.6%
E 1222356
 
5.6%
C 1085694
 
5.0%
Other values (25) 3837817
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21921273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4411816
20.1%
I 2769839
12.6%
U 1603758
 
7.3%
T 1514641
 
6.9%
L 1511166
 
6.9%
N 1433658
 
6.5%
R 1293458
 
5.9%
V 1237070
 
5.6%
E 1222356
 
5.6%
C 1085694
 
5.0%
Other values (25) 3837817
17.5%

zona
Categorical

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 MiB
SANTANDER
739403 
LA GUAJIRA
572599 
TOLIMA
407942 
ANTIOQUIA
226327 
RISARALDA
132899 
Other values (29)
489554 

Length

Max length15
Median length12
Mean length8.6370447
Min length4

Characters and Unicode

Total characters22186184
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSANTANDER
2nd rowLA GUAJIRA
3rd rowLA GUAJIRA
4th rowLA GUAJIRA
5th rowLA GUAJIRA

Common Values

ValueCountFrequency (%)
SANTANDER 739403
28.8%
LA GUAJIRA 572599
22.3%
TOLIMA 407942
15.9%
ANTIOQUIA 226327
 
8.8%
RISARALDA 132899
 
5.2%
CUNDINAMARCA 107104
 
4.2%
NORTE SANTANDER 59121
 
2.3%
BOYACA 50586
 
2.0%
CAUCA 39420
 
1.5%
HUILA 36280
 
1.4%
Other values (24) 197043
 
7.7%

Length

2025-04-06T00:55:22.957012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santander 798524
24.9%
la 572599
17.9%
guajira 572599
17.9%
tolima 407942
12.7%
antioquia 226327
 
7.1%
risaralda 132899
 
4.2%
cundinamarca 107104
 
3.3%
norte 59121
 
1.8%
boyaca 50586
 
1.6%
cauca 39420
 
1.2%
Other values (26) 233557
 
7.3%

Most occurring characters

ValueCountFrequency (%)
A 5370583
24.2%
N 2186344
9.9%
R 1880810
 
8.5%
I 1772480
 
8.0%
T 1589719
 
7.2%
L 1268729
 
5.7%
D 1062422
 
4.8%
U 994194
 
4.5%
S 973844
 
4.4%
E 959161
 
4.3%
Other values (20) 4127898
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22186184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5370583
24.2%
N 2186344
9.9%
R 1880810
 
8.5%
I 1772480
 
8.0%
T 1589719
 
7.2%
L 1268729
 
5.7%
D 1062422
 
4.8%
U 994194
 
4.5%
S 973844
 
4.4%
E 959161
 
4.3%
Other values (20) 4127898
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22186184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5370583
24.2%
N 2186344
9.9%
R 1880810
 
8.5%
I 1772480
 
8.0%
T 1589719
 
7.2%
L 1268729
 
5.7%
D 1062422
 
4.8%
U 994194
 
4.5%
S 973844
 
4.4%
E 959161
 
4.3%
Other values (20) 4127898
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22186184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5370583
24.2%
N 2186344
9.9%
R 1880810
 
8.5%
I 1772480
 
8.0%
T 1589719
 
7.2%
L 1268729
 
5.7%
D 1062422
 
4.8%
U 994194
 
4.5%
S 973844
 
4.4%
E 959161
 
4.3%
Other values (20) 4127898
18.6%

asesor
Text

Distinct608
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.5 MiB
2025-04-06T00:55:23.594754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.5900871
Min length8

Characters and Unicode

Total characters24634287
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowasesor_2
2nd rowasesor_3
3rd rowasesor_3
4th rowasesor_3
5th rowasesor_3
ValueCountFrequency (%)
asesor_137 42270
 
1.6%
asesor_251 40478
 
1.6%
asesor_7 39721
 
1.5%
asesor_197 32477
 
1.3%
asesor_122 31884
 
1.2%
asesor_9 31405
 
1.2%
asesor_103 30051
 
1.2%
asesor_255 29499
 
1.1%
asesor_397 28404
 
1.1%
asesor_2 27953
 
1.1%
Other values (598) 2234582
87.0%
2025-04-06T00:55:24.875944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 5137448
20.9%
a 2568724
10.4%
e 2568724
10.4%
o 2568724
10.4%
r 2568724
10.4%
_ 2568724
10.4%
1 1379679
 
5.6%
2 1054242
 
4.3%
3 789343
 
3.2%
7 612227
 
2.5%
Other values (6) 2817728
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24634287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 5137448
20.9%
a 2568724
10.4%
e 2568724
10.4%
o 2568724
10.4%
r 2568724
10.4%
_ 2568724
10.4%
1 1379679
 
5.6%
2 1054242
 
4.3%
3 789343
 
3.2%
7 612227
 
2.5%
Other values (6) 2817728
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24634287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 5137448
20.9%
a 2568724
10.4%
e 2568724
10.4%
o 2568724
10.4%
r 2568724
10.4%
_ 2568724
10.4%
1 1379679
 
5.6%
2 1054242
 
4.3%
3 789343
 
3.2%
7 612227
 
2.5%
Other values (6) 2817728
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24634287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 5137448
20.9%
a 2568724
10.4%
e 2568724
10.4%
o 2568724
10.4%
r 2568724
10.4%
_ 2568724
10.4%
1 1379679
 
5.6%
2 1054242
 
4.3%
3 789343
 
3.2%
7 612227
 
2.5%
Other values (6) 2817728
11.4%
Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.3 MiB
2025-04-06T00:55:25.260958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.588146
Min length13

Characters and Unicode

Total characters34904198
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpunto_venta_2
2nd rowpunto_venta_2
3rd rowpunto_venta_2
4th rowpunto_venta_2
5th rowpunto_venta_2
ValueCountFrequency (%)
punto_venta_2 343666
 
13.4%
punto_venta_33 194569
 
7.6%
punto_venta_7 132891
 
5.2%
punto_venta_4 124219
 
4.8%
punto_venta_6 116909
 
4.6%
punto_venta_21 112599
 
4.4%
punto_venta_10 107327
 
4.2%
punto_venta_9 101799
 
4.0%
punto_venta_3 84689
 
3.3%
punto_venta_1 74783
 
2.9%
Other values (56) 1175273
45.8%
2025-04-06T00:55:26.294843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 5137448
14.7%
t 5137448
14.7%
_ 5137448
14.7%
p 2568724
7.4%
u 2568724
7.4%
o 2568724
7.4%
v 2568724
7.4%
e 2568724
7.4%
a 2568724
7.4%
2 999527
 
2.9%
Other values (9) 3079983
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34904198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 5137448
14.7%
t 5137448
14.7%
_ 5137448
14.7%
p 2568724
7.4%
u 2568724
7.4%
o 2568724
7.4%
v 2568724
7.4%
e 2568724
7.4%
a 2568724
7.4%
2 999527
 
2.9%
Other values (9) 3079983
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34904198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 5137448
14.7%
t 5137448
14.7%
_ 5137448
14.7%
p 2568724
7.4%
u 2568724
7.4%
o 2568724
7.4%
v 2568724
7.4%
e 2568724
7.4%
a 2568724
7.4%
2 999527
 
2.9%
Other values (9) 3079983
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34904198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 5137448
14.7%
t 5137448
14.7%
_ 5137448
14.7%
p 2568724
7.4%
u 2568724
7.4%
o 2568724
7.4%
v 2568724
7.4%
e 2568724
7.4%
a 2568724
7.4%
2 999527
 
2.9%
Other values (9) 3079983
8.8%

cluster
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.2 MiB
cluster_tienda_3
1158447 
cluster_tienda_2
1071931 
cluster_tienda_1
209912 
cluster_tienda_4
 
98969
cluster_tienda_5
 
26635
Other values (4)
 
2830

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters41099584
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcluster_tienda_2
2nd rowcluster_tienda_2
3rd rowcluster_tienda_2
4th rowcluster_tienda_2
5th rowcluster_tienda_2

Common Values

ValueCountFrequency (%)
cluster_tienda_3 1158447
45.1%
cluster_tienda_2 1071931
41.7%
cluster_tienda_1 209912
 
8.2%
cluster_tienda_4 98969
 
3.9%
cluster_tienda_5 26635
 
1.0%
cluster_tienda_6 1246
 
< 0.1%
cluster_tienda_8 830
 
< 0.1%
cluster_tienda_7 753
 
< 0.1%
cluster_tienda_9 1
 
< 0.1%

Length

2025-04-06T00:55:26.459323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T00:55:26.610554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cluster_tienda_3 1158447
45.1%
cluster_tienda_2 1071931
41.7%
cluster_tienda_1 209912
 
8.2%
cluster_tienda_4 98969
 
3.9%
cluster_tienda_5 26635
 
1.0%
cluster_tienda_6 1246
 
< 0.1%
cluster_tienda_8 830
 
< 0.1%
cluster_tienda_7 753
 
< 0.1%
cluster_tienda_9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 5137448
12.5%
e 5137448
12.5%
_ 5137448
12.5%
c 2568724
 
6.2%
i 2568724
 
6.2%
a 2568724
 
6.2%
l 2568724
 
6.2%
n 2568724
 
6.2%
d 2568724
 
6.2%
r 2568724
 
6.2%
Other values (11) 7706172
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41099584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 5137448
12.5%
e 5137448
12.5%
_ 5137448
12.5%
c 2568724
 
6.2%
i 2568724
 
6.2%
a 2568724
 
6.2%
l 2568724
 
6.2%
n 2568724
 
6.2%
d 2568724
 
6.2%
r 2568724
 
6.2%
Other values (11) 7706172
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41099584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 5137448
12.5%
e 5137448
12.5%
_ 5137448
12.5%
c 2568724
 
6.2%
i 2568724
 
6.2%
a 2568724
 
6.2%
l 2568724
 
6.2%
n 2568724
 
6.2%
d 2568724
 
6.2%
r 2568724
 
6.2%
Other values (11) 7706172
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41099584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 5137448
12.5%
e 5137448
12.5%
_ 5137448
12.5%
c 2568724
 
6.2%
i 2568724
 
6.2%
a 2568724
 
6.2%
l 2568724
 
6.2%
n 2568724
 
6.2%
d 2568724
 
6.2%
r 2568724
 
6.2%
Other values (11) 7706172
18.8%

categoria_macro
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.7 MiB
categoria_macro_2
1602004 
categoria_macro_4
620206 
categoria_macro_1
221863 
categoria_macro_3
 
112996
categoria_macro_5
 
11655

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters43668308
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcategoria_macro_1
2nd rowcategoria_macro_2
3rd rowcategoria_macro_2
4th rowcategoria_macro_2
5th rowcategoria_macro_2

Common Values

ValueCountFrequency (%)
categoria_macro_2 1602004
62.4%
categoria_macro_4 620206
 
24.1%
categoria_macro_1 221863
 
8.6%
categoria_macro_3 112996
 
4.4%
categoria_macro_5 11655
 
0.5%

Length

2025-04-06T00:55:26.856471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T00:55:27.093590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
categoria_macro_2 1602004
62.4%
categoria_macro_4 620206
 
24.1%
categoria_macro_1 221863
 
8.6%
categoria_macro_3 112996
 
4.4%
categoria_macro_5 11655
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 7706172
17.6%
c 5137448
11.8%
o 5137448
11.8%
r 5137448
11.8%
_ 5137448
11.8%
t 2568724
 
5.9%
e 2568724
 
5.9%
g 2568724
 
5.9%
i 2568724
 
5.9%
m 2568724
 
5.9%
Other values (5) 2568724
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43668308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7706172
17.6%
c 5137448
11.8%
o 5137448
11.8%
r 5137448
11.8%
_ 5137448
11.8%
t 2568724
 
5.9%
e 2568724
 
5.9%
g 2568724
 
5.9%
i 2568724
 
5.9%
m 2568724
 
5.9%
Other values (5) 2568724
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43668308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7706172
17.6%
c 5137448
11.8%
o 5137448
11.8%
r 5137448
11.8%
_ 5137448
11.8%
t 2568724
 
5.9%
e 2568724
 
5.9%
g 2568724
 
5.9%
i 2568724
 
5.9%
m 2568724
 
5.9%
Other values (5) 2568724
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43668308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7706172
17.6%
c 5137448
11.8%
o 5137448
11.8%
r 5137448
11.8%
_ 5137448
11.8%
t 2568724
 
5.9%
e 2568724
 
5.9%
g 2568724
 
5.9%
i 2568724
 
5.9%
m 2568724
 
5.9%
Other values (5) 2568724
 
5.9%

categoria
Categorical

High correlation 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.5 MiB
categoria_3
566967 
categoria_7
535182 
categoria_5
359200 
categoria_11
195663 
categoria_1
176468 
Other values (22)
735244 

Length

Max length12
Median length11
Mean length11.213218
Min length11

Characters and Unicode

Total characters28803661
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcategoria_2
2nd rowcategoria_3
3rd rowcategoria_3
4th rowcategoria_3
5th rowcategoria_3

Common Values

ValueCountFrequency (%)
categoria_3 566967
22.1%
categoria_7 535182
20.8%
categoria_5 359200
14.0%
categoria_11 195663
 
7.6%
categoria_1 176468
 
6.9%
categoria_12 162338
 
6.3%
categoria_8 138936
 
5.4%
categoria_9 100276
 
3.9%
categoria_10 65163
 
2.5%
categoria_6 59967
 
2.3%
Other values (17) 208564
 
8.1%

Length

2025-04-06T00:55:27.298591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
categoria_3 566967
22.1%
categoria_7 535182
20.8%
categoria_5 359200
14.0%
categoria_11 195663
 
7.6%
categoria_1 176468
 
6.9%
categoria_12 162338
 
6.3%
categoria_8 138936
 
5.4%
categoria_9 100276
 
3.9%
categoria_10 65163
 
2.5%
categoria_6 59967
 
2.3%
Other values (17) 208564
 
8.1%

Most occurring characters

ValueCountFrequency (%)
a 5137448
17.8%
c 2568724
8.9%
t 2568724
8.9%
e 2568724
8.9%
g 2568724
8.9%
o 2568724
8.9%
r 2568724
8.9%
i 2568724
8.9%
_ 2568724
8.9%
1 895544
 
3.1%
Other values (9) 2220877
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28803661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5137448
17.8%
c 2568724
8.9%
t 2568724
8.9%
e 2568724
8.9%
g 2568724
8.9%
o 2568724
8.9%
r 2568724
8.9%
i 2568724
8.9%
_ 2568724
8.9%
1 895544
 
3.1%
Other values (9) 2220877
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28803661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5137448
17.8%
c 2568724
8.9%
t 2568724
8.9%
e 2568724
8.9%
g 2568724
8.9%
o 2568724
8.9%
r 2568724
8.9%
i 2568724
8.9%
_ 2568724
8.9%
1 895544
 
3.1%
Other values (9) 2220877
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28803661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5137448
17.8%
c 2568724
8.9%
t 2568724
8.9%
e 2568724
8.9%
g 2568724
8.9%
o 2568724
8.9%
r 2568724
8.9%
i 2568724
8.9%
_ 2568724
8.9%
1 895544
 
3.1%
Other values (9) 2220877
7.7%
Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.4 MiB
2025-04-06T00:55:27.624792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.426673
Min length14

Characters and Unicode

Total characters37058142
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowsubcategoria_2
2nd rowsubcategoria_3
3rd rowsubcategoria_3
4th rowsubcategoria_3
5th rowsubcategoria_3
ValueCountFrequency (%)
subcategoria_5 822252
32.0%
subcategoria_3 296439
 
11.5%
subcategoria_9 194553
 
7.6%
subcategoria_12 94339
 
3.7%
subcategoria_14 87146
 
3.4%
subcategoria_22 75970
 
3.0%
subcategoria_7 72048
 
2.8%
subcategoria_13 62574
 
2.4%
subcategoria_39 61477
 
2.4%
subcategoria_24 58349
 
2.3%
Other values (92) 743577
28.9%
2025-04-06T00:55:28.192468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5137448
13.9%
s 2568724
 
6.9%
g 2568724
 
6.9%
u 2568724
 
6.9%
i 2568724
 
6.9%
r 2568724
 
6.9%
o 2568724
 
6.9%
_ 2568724
 
6.9%
e 2568724
 
6.9%
t 2568724
 
6.9%
Other values (12) 8802178
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37058142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5137448
13.9%
s 2568724
 
6.9%
g 2568724
 
6.9%
u 2568724
 
6.9%
i 2568724
 
6.9%
r 2568724
 
6.9%
o 2568724
 
6.9%
_ 2568724
 
6.9%
e 2568724
 
6.9%
t 2568724
 
6.9%
Other values (12) 8802178
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37058142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5137448
13.9%
s 2568724
 
6.9%
g 2568724
 
6.9%
u 2568724
 
6.9%
i 2568724
 
6.9%
r 2568724
 
6.9%
o 2568724
 
6.9%
_ 2568724
 
6.9%
e 2568724
 
6.9%
t 2568724
 
6.9%
Other values (12) 8802178
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37058142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5137448
13.9%
s 2568724
 
6.9%
g 2568724
 
6.9%
u 2568724
 
6.9%
i 2568724
 
6.9%
r 2568724
 
6.9%
o 2568724
 
6.9%
_ 2568724
 
6.9%
e 2568724
 
6.9%
t 2568724
 
6.9%
Other values (12) 8802178
23.8%
Distinct7263
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size149.3 MiB
2025-04-06T00:55:28.758465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.963088
Min length10

Characters and Unicode

Total characters30729870
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique958 ?
Unique (%)< 0.1%

Sample

1st rowproducto_2
2nd rowproducto_3
3rd rowproducto_3
4th rowproducto_3
5th rowproducto_3
ValueCountFrequency (%)
producto_19 69248
 
2.7%
producto_49 61396
 
2.4%
producto_40 32927
 
1.3%
producto_28 32313
 
1.3%
producto_176 31598
 
1.2%
producto_3 30504
 
1.2%
producto_110 30023
 
1.2%
producto_72 27744
 
1.1%
producto_119 26915
 
1.0%
producto_112 25300
 
1.0%
Other values (7253) 2200756
85.7%
2025-04-06T00:55:29.378135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5137448
16.7%
p 2568724
8.4%
d 2568724
8.4%
u 2568724
8.4%
c 2568724
8.4%
t 2568724
8.4%
_ 2568724
8.4%
r 2568724
8.4%
1 1374583
 
4.5%
2 911738
 
3.0%
Other values (8) 5325033
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30729870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5137448
16.7%
p 2568724
8.4%
d 2568724
8.4%
u 2568724
8.4%
c 2568724
8.4%
t 2568724
8.4%
_ 2568724
8.4%
r 2568724
8.4%
1 1374583
 
4.5%
2 911738
 
3.0%
Other values (8) 5325033
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30729870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5137448
16.7%
p 2568724
8.4%
d 2568724
8.4%
u 2568724
8.4%
c 2568724
8.4%
t 2568724
8.4%
_ 2568724
8.4%
r 2568724
8.4%
1 1374583
 
4.5%
2 911738
 
3.0%
Other values (8) 5325033
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30729870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5137448
16.7%
p 2568724
8.4%
d 2568724
8.4%
u 2568724
8.4%
c 2568724
8.4%
t 2568724
8.4%
_ 2568724
8.4%
r 2568724
8.4%
1 1374583
 
4.5%
2 911738
 
3.0%
Other values (8) 5325033
17.3%

color
Text

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.8 MiB
2025-04-06T00:55:29.749620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.8936266
Min length3

Characters and Unicode

Total characters22845272
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowGRIS
2nd rowBEIGE
3rd rowBEIGE
4th rowBEIGE
5th rowBEIGE
ValueCountFrequency (%)
no 1196257
31.8%
encontrado 1196257
31.8%
gris 515685
13.7%
blanco 303687
 
8.1%
beige 220542
 
5.9%
multicolor 106369
 
2.8%
marfil 54243
 
1.4%
negro 48494
 
1.3%
azul 35037
 
0.9%
mate 18145
 
0.5%
Other values (59) 70265
 
1.9%
2025-04-06T00:55:30.107967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3588771
15.7%
n 2392514
 
10.5%
N 1575543
 
6.9%
1196257
 
5.2%
e 1196257
 
5.2%
c 1196257
 
5.2%
t 1196257
 
5.2%
r 1196257
 
5.2%
a 1196257
 
5.2%
d 1196257
 
5.2%
Other values (26) 6914645
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22845272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3588771
15.7%
n 2392514
 
10.5%
N 1575543
 
6.9%
1196257
 
5.2%
e 1196257
 
5.2%
c 1196257
 
5.2%
t 1196257
 
5.2%
r 1196257
 
5.2%
a 1196257
 
5.2%
d 1196257
 
5.2%
Other values (26) 6914645
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22845272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3588771
15.7%
n 2392514
 
10.5%
N 1575543
 
6.9%
1196257
 
5.2%
e 1196257
 
5.2%
c 1196257
 
5.2%
t 1196257
 
5.2%
r 1196257
 
5.2%
a 1196257
 
5.2%
d 1196257
 
5.2%
Other values (26) 6914645
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22845272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3588771
15.7%
n 2392514
 
10.5%
N 1575543
 
6.9%
1196257
 
5.2%
e 1196257
 
5.2%
c 1196257
 
5.2%
t 1196257
 
5.2%
r 1196257
 
5.2%
a 1196257
 
5.2%
d 1196257
 
5.2%
Other values (26) 6914645
30.3%

cantidad
Real number (ℝ)

High correlation  Skewed 

Distinct6229
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.601075
Minimum0.15
Maximum489689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:30.350865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile1
Q11
median3
Q312.8
95-th percentile140.14
Maximum489689
Range489688.85
Interquartile range (IQR)11.8

Descriptive statistics

Standard deviation680.68267
Coefficient of variation (CV)18.597341
Kurtosis238348.65
Mean36.601075
Median Absolute Deviation (MAD)2
Skewness407.67185
Sum94018061
Variance463328.9
MonotonicityNot monotonic
2025-04-06T00:55:30.513523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 725909
28.3%
2 327616
 
12.8%
5 91126
 
3.5%
25 59469
 
2.3%
4 57452
 
2.2%
3 49614
 
1.9%
10 42112
 
1.6%
50 39743
 
1.5%
6 26796
 
1.0%
3.2 26434
 
1.0%
Other values (6219) 1122453
43.7%
ValueCountFrequency (%)
0.15 1
 
< 0.1%
0.33 1
 
< 0.1%
0.4 33
 
< 0.1%
0.42 1
 
< 0.1%
0.48 91
< 0.1%
0.5 13
 
< 0.1%
0.52 16
 
< 0.1%
0.54 38
< 0.1%
0.57 1
 
< 0.1%
0.61 1
 
< 0.1%
ValueCountFrequency (%)
489689 1
< 0.1%
477673 1
< 0.1%
264542 1
< 0.1%
262179 1
< 0.1%
251039 1
< 0.1%
246562 1
< 0.1%
189346 1
< 0.1%
167730 1
< 0.1%
162539 1
< 0.1%
130591 1
< 0.1%

precio
Real number (ℝ)

High correlation  Skewed 

Distinct9635
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0983616
Minimum0
Maximum12043.48
Zeros67
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:30.825206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.65
median3.01
Q36.16
95-th percentile43
Maximum12043.48
Range12043.48
Interquartile range (IQR)5.51

Descriptive statistics

Standard deviation28.918855
Coefficient of variation (CV)3.1784684
Kurtosis18258.202
Mean9.0983616
Median Absolute Deviation (MAD)2.41
Skewness75.645332
Sum23371180
Variance836.30017
MonotonicityNot monotonic
2025-04-06T00:55:31.125124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56 54220
 
2.1%
0.6 47463
 
1.8%
0.5 45569
 
1.8%
0.47 40941
 
1.6%
0.07 36285
 
1.4%
0.65 35864
 
1.4%
0.15 31009
 
1.2%
0.11 27962
 
1.1%
0.13 26860
 
1.0%
0.58 24410
 
1.0%
Other values (9625) 2198141
85.6%
ValueCountFrequency (%)
0 67
 
< 0.1%
0.01 2
 
< 0.1%
0.04 373
 
< 0.1%
0.05 60
 
< 0.1%
0.06 2832
 
0.1%
0.07 36285
1.4%
0.08 23595
0.9%
0.09 23979
0.9%
0.1 3888
 
0.2%
0.11 27962
1.1%
ValueCountFrequency (%)
12043.48 1
 
< 0.1%
6451.52 1
 
< 0.1%
5833.75 1
 
< 0.1%
5592.58 1
 
< 0.1%
5591.54 4
< 0.1%
4955.1 1
 
< 0.1%
4816.04 1
 
< 0.1%
4099.78 1
 
< 0.1%
4044.55 1
 
< 0.1%
3846.93 2
< 0.1%

valor
Real number (ℝ)

High correlation  Skewed 

Distinct46578
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.472455
Minimum0.07
Maximum56876.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:31.358406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile1.14
Q14.69
median12.9
Q336.2
95-th percentile142.1785
Maximum56876.09
Range56876.02
Interquartile range (IQR)31.51

Descriptive statistics

Standard deviation153.75788
Coefficient of variation (CV)3.9965705
Kurtosis20340.008
Mean38.472455
Median Absolute Deviation (MAD)10.48
Skewness88.626893
Sum98825118
Variance23641.485
MonotonicityNot monotonic
2025-04-06T00:55:31.674965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13 42912
 
1.7%
1.2 40207
 
1.6%
2.34 25645
 
1.0%
1.3 22891
 
0.9%
2.49 20050
 
0.8%
0.35 16851
 
0.7%
1.17 16328
 
0.6%
2.67 11964
 
0.5%
2.5 11937
 
0.5%
4.69 9564
 
0.4%
Other values (46568) 2350375
91.5%
ValueCountFrequency (%)
0.07 32
 
< 0.1%
0.08 2
 
< 0.1%
0.09 22
 
< 0.1%
0.1 39
 
< 0.1%
0.11 19
 
< 0.1%
0.12 40
 
< 0.1%
0.13 235
< 0.1%
0.14 32
 
< 0.1%
0.15 86
 
< 0.1%
0.16 10
 
< 0.1%
ValueCountFrequency (%)
56876.09 1
< 0.1%
55491.7 1
< 0.1%
32647.37 1
< 0.1%
32398.94 1
< 0.1%
30571.52 1
< 0.1%
30508.79 1
< 0.1%
30434.9 1
< 0.1%
29158.44 1
< 0.1%
28599.27 1
< 0.1%
24053.41 1
< 0.1%

alineación con portafolio estratégico
Real number (ℝ)

High correlation  Skewed 

Distinct24003
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8254732
Minimum-24187.463
Maximum4162.2266
Zeros1672
Zeros (%)0.1%
Negative1520
Negative (%)0.1%
Memory size19.6 MiB
2025-04-06T00:55:31.828517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-24187.463
5-th percentile0.117504
Q10.46656
median1.3824
Q33.652992
95-th percentile13.78944
Maximum4162.2266
Range28349.69
Interquartile range (IQR)3.186432

Descriptive statistics

Standard deviation20.320648
Coefficient of variation (CV)5.3119306
Kurtosis784122.68
Mean3.8254732
Median Absolute Deviation (MAD)1.1232
Skewness-638.33381
Sum9826584.8
Variance412.92873
MonotonicityNot monotonic
2025-04-06T00:55:32.045953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.124416 45432
 
1.8%
0.117504 42682
 
1.7%
0.2592 31017
 
1.2%
0.134784 26327
 
1.0%
0.048384 23665
 
0.9%
0.245376 23210
 
0.9%
0.12096 15691
 
0.6%
0.487296 13989
 
0.5%
0.27648 13468
 
0.5%
0.5184 10708
 
0.4%
Other values (23993) 2322535
90.4%
ValueCountFrequency (%)
-24187.463 1
 
< 0.1%
-1712.1749 1
 
< 0.1%
-796.1898 1
 
< 0.1%
-701.06 1
 
< 0.1%
-684.16016 1
 
< 0.1%
-497.7262 1
 
< 0.1%
-455.72888 1
 
< 0.1%
-215.04614 1
 
< 0.1%
-193.44269 1
 
< 0.1%
-78.08832 4
< 0.1%
ValueCountFrequency (%)
4162.2266 1
 
< 0.1%
3887.008 1
 
< 0.1%
2969.6543 1
 
< 0.1%
2900.6692 1
 
< 0.1%
2229.6453 1
 
< 0.1%
2158.8525 1
 
< 0.1%
2016.144 1
 
< 0.1%
1932.7957 1
 
< 0.1%
1932.4363 4
< 0.1%
1788.9984 1
 
< 0.1%

log_cantidad
Real number (ℝ)

High correlation 

Distinct6229
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9180917
Minimum0.13976195
Maximum13.101528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:32.258350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.13976195
5-th percentile0.6931472
Q10.6931472
median1.3862944
Q32.6246686
95-th percentile4.9497523
Maximum13.101528
Range12.961766
Interquartile range (IQR)1.9315214

Descriptive statistics

Standard deviation1.4139768
Coefficient of variation (CV)0.73717895
Kurtosis1.4442283
Mean1.9180917
Median Absolute Deviation (MAD)0.6931472
Skewness1.3756699
Sum4927048.2
Variance1.9993304
MonotonicityNot monotonic
2025-04-06T00:55:32.494283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6931472 725909
28.3%
1.0986123 327616
 
12.8%
1.7917595 91126
 
3.5%
3.2580965 59469
 
2.3%
1.609438 57452
 
2.2%
1.3862944 49614
 
1.9%
2.3978953 42112
 
1.6%
3.9318256 39743
 
1.5%
1.9459101 26796
 
1.0%
1.4350845 26434
 
1.0%
Other values (6219) 1122453
43.7%
ValueCountFrequency (%)
0.13976195 1
 
< 0.1%
0.28517896 1
 
< 0.1%
0.33647224 33
 
< 0.1%
0.35065687 1
 
< 0.1%
0.39204207 91
< 0.1%
0.4054651 13
 
< 0.1%
0.41871032 16
 
< 0.1%
0.43178242 38
< 0.1%
0.4510756 1
 
< 0.1%
0.4762342 1
 
< 0.1%
ValueCountFrequency (%)
13.101528 1
< 0.1%
13.076684 1
< 0.1%
12.485759 1
< 0.1%
12.476787 1
< 0.1%
12.433368 1
< 0.1%
12.415373 1
< 0.1%
12.151337 1
< 0.1%
12.030117 1
< 0.1%
11.998679 1
< 0.1%
11.779833 1
< 0.1%

log_precio
Real number (ℝ)

High correlation 

Distinct9635
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5204145
Minimum0
Maximum9.396361
Zeros67
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:32.652290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11332868
Q10.5007753
median1.3887913
Q31.9685099
95-th percentile3.7841897
Maximum9.396361
Range9.396361
Interquartile range (IQR)1.4677346

Descriptive statistics

Standard deviation1.0945442
Coefficient of variation (CV)0.71989856
Kurtosis0.48589673
Mean1.5204145
Median Absolute Deviation (MAD)0.6981223
Skewness0.90810704
Sum3905525.3
Variance1.198027
MonotonicityNot monotonic
2025-04-06T00:55:32.840101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44468582 54220
 
2.1%
0.47000363 47463
 
1.8%
0.4054651 45569
 
1.8%
0.3852624 40941
 
1.6%
0.06765865 36285
 
1.4%
0.5007753 35864
 
1.4%
0.13976195 31009
 
1.2%
0.104360014 27962
 
1.1%
0.122217625 26860
 
1.0%
0.45742482 24410
 
1.0%
Other values (9625) 2198141
85.6%
ValueCountFrequency (%)
0 67
 
< 0.1%
0.0099503305 2
 
< 0.1%
0.039220713 373
 
< 0.1%
0.048790164 60
 
< 0.1%
0.058268905 2832
 
0.1%
0.06765865 36285
1.4%
0.07696103 23595
0.9%
0.0861777 23979
0.9%
0.09531018 3888
 
0.2%
0.104360014 27962
1.1%
ValueCountFrequency (%)
9.396361 1
 
< 0.1%
8.772226 1
 
< 0.1%
8.671587 1
 
< 0.1%
8.6293745 1
 
< 0.1%
8.629189 4
< 0.1%
8.508374 1
 
< 0.1%
8.479915 1
 
< 0.1%
8.318933 1
 
< 0.1%
8.305373 1
 
< 0.1%
8.255291 2
< 0.1%

log_valor
Real number (ℝ)

High correlation 

Distinct46578
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7006973
Minimum0.06765865
Maximum10.948647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:32.958496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.06765865
5-th percentile0.76080585
Q11.7387103
median2.6318889
Q33.6163087
95-th percentile4.9640922
Maximum10.948647
Range10.880989
Interquartile range (IQR)1.8775984

Descriptive statistics

Standard deviation1.3098293
Coefficient of variation (CV)0.48499673
Kurtosis-0.25988857
Mean2.7006973
Median Absolute Deviation (MAD)0.9362734
Skewness0.35403333
Sum6937345.9
Variance1.7156529
MonotonicityNot monotonic
2025-04-06T00:55:33.213137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.756122 42912
 
1.7%
0.7884574 40207
 
1.6%
1.2059708 25645
 
1.0%
0.8329091 22891
 
0.9%
1.2499018 20050
 
0.8%
0.3001046 16851
 
0.7%
0.77472717 16328
 
0.6%
1.3001916 11964
 
0.5%
1.2527629 11937
 
0.5%
1.7387103 9564
 
0.4%
Other values (46568) 2350375
91.5%
ValueCountFrequency (%)
0.06765865 32
 
< 0.1%
0.07696103 2
 
< 0.1%
0.0861777 22
 
< 0.1%
0.09531018 39
 
< 0.1%
0.104360014 19
 
< 0.1%
0.11332868 40
 
< 0.1%
0.122217625 235
< 0.1%
0.13102826 32
 
< 0.1%
0.13976195 86
 
< 0.1%
0.14842 10
 
< 0.1%
ValueCountFrequency (%)
10.9486475 1
< 0.1%
10.924006 1
< 0.1%
10.39355 1
< 0.1%
10.385912 1
< 0.1%
10.327857 1
< 0.1%
10.325803 1
< 0.1%
10.323379 1
< 0.1%
10.280534 1
< 0.1%
10.261171 1
< 0.1%
10.088074 1
< 0.1%

ajustada_alineacion
Real number (ℝ)

High correlation  Skewed 

Distinct24003
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0067968525
Minimum-771.33545
Maximum276.4262
Zeros0
Zeros (%)0.0%
Negative1997360
Negative (%)77.8%
Memory size19.6 MiB
2025-04-06T00:55:33.391979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-771.33545
5-th percentile-0.2011434
Q1-0.184258
median-0.13917093
Q3-0.024373926
95-th percentile0.5112163
Maximum276.4262
Range1047.7616
Interquartile range (IQR)0.15988407

Descriptive statistics

Standard deviation0.94209111
Coefficient of variation (CV)-138.60697
Kurtosis183559.67
Mean-0.0067968525
Median Absolute Deviation (MAD)0.05514289
Skewness-164.04417
Sum-17459.238
Variance0.88753565
MonotonicityNot monotonic
2025-04-06T00:55:33.691676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2008111 45432
 
1.8%
-0.2011434 42682
 
1.7%
-0.19431382 31017
 
1.2%
-0.20031251 26327
 
1.0%
-0.20446117 23665
 
0.9%
-0.1949817 23210
 
0.9%
-0.20097728 15691
 
0.6%
-0.18324877 13989
 
0.5%
-0.1934785 13468
 
0.5%
-0.18173373 10708
 
0.4%
Other values (23993) 2322535
90.4%
ValueCountFrequency (%)
-771.33545 1
 
< 0.1%
-61.64089 1
 
< 0.1%
-29.762724 1
 
< 0.1%
-26.37837 1
 
< 0.1%
-25.775064 1
 
< 0.1%
-19.070955 1
 
< 0.1%
-17.54647 1
 
< 0.1%
-8.662593 1
 
< 0.1%
-7.8481994 1
 
< 0.1%
-3.4135826 4
< 0.1%
ValueCountFrequency (%)
276.4262 1
 
< 0.1%
257.34677 1
 
< 0.1%
194.21335 1
 
< 0.1%
189.49796 1
 
< 0.1%
143.91191 1
 
< 0.1%
139.13548 1
 
< 0.1%
129.52835 1
 
< 0.1%
123.93126 1
 
< 0.1%
123.90715 4
< 0.1%
114.30045 1
 
< 0.1%

edad_promedio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing4954
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean35.674954
Minimum28.311825
Maximum41.152468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:33.974883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum28.311825
5-th percentile29.862683
Q134.193915
median37.077072
Q338.001476
95-th percentile39.272306
Maximum41.152468
Range12.840643
Interquartile range (IQR)3.8075606

Descriptive statistics

Standard deviation3.3572433
Coefficient of variation (CV)0.094106449
Kurtosis-0.72522187
Mean35.674954
Median Absolute Deviation (MAD)1.5686651
Skewness-0.93713351
Sum91462377
Variance11.271082
MonotonicityNot monotonic
2025-04-06T00:55:34.241016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
37.07707192 739403
28.8%
29.86268292 572599
22.3%
38.64573702 407942
15.9%
38.00147566 226327
 
8.8%
39.27230572 132899
 
5.2%
36.7491788 107104
 
4.2%
34.96857356 59121
 
2.3%
36.26800506 50586
 
2.0%
36.84756013 39420
 
1.5%
35.76903746 36280
 
1.4%
Other values (22) 192089
 
7.5%
ValueCountFrequency (%)
28.31182473 27
 
< 0.1%
28.83482587 180
 
< 0.1%
29.86268292 572599
22.3%
31.20435967 26107
 
1.0%
31.66406992 16
 
< 0.1%
31.7555456 430
 
< 0.1%
32.10022272 793
 
< 0.1%
32.22589259 4018
 
0.2%
32.31182538 177
 
< 0.1%
33.17039184 1034
 
< 0.1%
ValueCountFrequency (%)
41.15246802 8184
 
0.3%
40.63623799 5574
 
0.2%
39.27230572 132899
 
5.2%
38.64573702 407942
15.9%
38.00147566 226327
 
8.8%
37.71364425 20181
 
0.8%
37.68387216 27727
 
1.1%
37.46664198 159
 
< 0.1%
37.28770028 234
 
< 0.1%
37.07707192 739403
28.8%

ingreso_laboral_promedio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing4954
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1616382.6
Minimum1171393.5
Maximum2593276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:34.374743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1171393.5
5-th percentile1212095.3
Q11404703.6
median1577079.1
Q31801097.3
95-th percentile2107814
Maximum2593276
Range1421882.5
Interquartile range (IQR)396393.75

Descriptive statistics

Standard deviation278703.85
Coefficient of variation (CV)0.17242443
Kurtosis-0.98872664
Mean1616382.6
Median Absolute Deviation (MAD)224018.21
Skewness-0.082277501
Sum4.1440333 × 1012
Variance7.7675838 × 1010
MonotonicityNot monotonic
2025-04-06T00:55:34.553671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1801097.339 739403
28.8%
1212095.342 572599
22.3%
1550360.891 407942
15.9%
2107813.959 226327
 
8.8%
1821178.566 132899
 
5.2%
1507068.661 107104
 
4.2%
1404703.587 59121
 
2.3%
1839749.174 50586
 
2.0%
1417425.912 39420
 
1.5%
1577079.13 36280
 
1.4%
Other values (22) 192089
 
7.5%
ValueCountFrequency (%)
1171393.493 3198
 
0.1%
1212095.342 572599
22.3%
1243626.566 793
 
< 0.1%
1307811.243 4018
 
0.2%
1310843.249 5591
 
0.2%
1348154.753 4285
 
0.2%
1388253.883 18671
 
0.7%
1404703.587 59121
 
2.3%
1417425.912 39420
 
1.5%
1434209.509 20181
 
0.8%
ValueCountFrequency (%)
2593275.96 159
 
< 0.1%
2229208.752 16
 
< 0.1%
2107813.959 226327
 
8.8%
2004599.926 26107
 
1.0%
1901364.142 8184
 
0.3%
1866250.314 234
 
< 0.1%
1839749.174 50586
 
2.0%
1831205.93 33648
 
1.3%
1821178.566 132899
 
5.2%
1801097.339 739403
28.8%

porcentaje_urbano
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)< 0.1%
Missing4954
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean84.754443
Minimum67.91
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:34.678334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum67.91
5-th percentile80.7
Q183.79
median86.48
Q386.88
95-th percentile87.94
Maximum100
Range32.09
Interquartile range (IQR)3.09

Descriptive statistics

Standard deviation4.5114472
Coefficient of variation (CV)0.053229625
Kurtosis6.7473447
Mean84.754443
Median Absolute Deviation (MAD)1.69
Skewness-1.7625147
Sum2.172909 × 108
Variance20.353156
MonotonicityNot monotonic
2025-04-06T00:55:34.825105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
86.88 739403
28.8%
84.79 572599
22.3%
80.7 407942
15.9%
87.64 226327
 
8.8%
87.94 132899
 
5.2%
67.91 107104
 
4.2%
87.54 59121
 
2.3%
83.43 50586
 
2.0%
83.79 39420
 
1.5%
84.45 36280
 
1.4%
Other values (14) 192089
 
7.5%
ValueCountFrequency (%)
67.91 107104
 
4.2%
80.7 407942
15.9%
83.12 4285
 
0.2%
83.43 50586
 
2.0%
83.56 33648
 
1.3%
83.79 39420
 
1.5%
84.26 1034
 
< 0.1%
84.45 36280
 
1.4%
84.79 572599
22.3%
85.64 20181
 
0.8%
ValueCountFrequency (%)
100 28710
 
1.1%
97.5 159
 
< 0.1%
88.81 31109
 
1.2%
88.75 5574
 
0.2%
88.46 27727
 
1.1%
87.94 132899
 
5.2%
87.64 226327
 
8.8%
87.54 59121
 
2.3%
87.09 18671
 
0.7%
86.88 739403
28.8%

IPUG
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean840471.69
Minimum395366
Maximum1935816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:35.010609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum395366
5-th percentile395366
Q1595819
median956065
Q31056184
95-th percentile1250932
Maximum1935816
Range1540450
Interquartile range (IQR)460365

Descriptive statistics

Standard deviation288446.6
Coefficient of variation (CV)0.3431961
Kurtosis-1.1850811
Mean840471.69
Median Absolute Deviation (MAD)185607
Skewness-0.41928982
Sum2.1589356 × 1012
Variance8.3201443 × 1010
MonotonicityNot monotonic
2025-04-06T00:55:35.207336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1056184 739403
28.8%
395366 572599
22.3%
770458 407942
15.9%
1250932 226327
 
8.8%
1049362 132899
 
5.2%
1096893 107104
 
4.2%
753265 59121
 
2.3%
811720 50586
 
2.0%
595819 39420
 
1.5%
684916 36280
 
1.4%
Other values (15) 197038
 
7.7%
ValueCountFrequency (%)
395366 572599
22.3%
416159 4949
 
0.2%
529491 3198
 
0.1%
550320 28710
 
1.1%
565106 4285
 
0.2%
585909 4018
 
0.2%
595819 39420
 
1.5%
612838 5591
 
0.2%
643962 1034
 
< 0.1%
680424 18671
 
0.7%
ValueCountFrequency (%)
1935816 159
 
< 0.1%
1250932 226327
 
8.8%
1102287 27727
 
1.1%
1096893 107104
 
4.2%
1082481 8184
 
0.3%
1056184 739403
28.8%
1049362 132899
 
5.2%
1002858 5574
 
0.2%
980735 31109
 
1.2%
956065 33648
 
1.3%

GINI
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.5143927
Minimum0.442
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:35.358320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.442
5-th percentile0.471
Q10.501
median0.512
Q30.524
95-th percentile0.536
Maximum0.95
Range0.508
Interquartile range (IQR)0.023

Descriptive statistics

Standard deviation0.038863934
Coefficient of variation (CV)0.075553044
Kurtosis48.723919
Mean0.5143927
Median Absolute Deviation (MAD)0.011
Skewness6.0249114
Sum1321330.3
Variance0.0015104054
MonotonicityNot monotonic
2025-04-06T00:55:35.525752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.501 739403
28.8%
0.536 572599
22.3%
0.518 407942
15.9%
0.524 226327
 
8.8%
0.471 225668
 
8.8%
0.506 107104
 
4.2%
0.512 58091
 
2.3%
0.498 50586
 
2.0%
0.472 36280
 
1.4%
0.486 31109
 
1.2%
Other values (18) 113610
 
4.4%
ValueCountFrequency (%)
0.442 1034
 
< 0.1%
0.465 5574
 
0.2%
0.471 225668
8.8%
0.472 36280
 
1.4%
0.486 31109
 
1.2%
0.487 5591
 
0.2%
0.491 8184
 
0.3%
0.492 3198
 
0.1%
0.497 20181
 
0.8%
0.498 50586
 
2.0%
ValueCountFrequency (%)
0.95 180
 
< 0.1%
0.84 26107
 
1.0%
0.71 1176
 
< 0.1%
0.67 234
 
< 0.1%
0.589 4949
 
0.2%
0.56 43
 
< 0.1%
0.54 793
 
< 0.1%
0.536 572599
22.3%
0.53 159
 
< 0.1%
0.524 226327
 
8.8%

total_productos
Real number (ℝ)

High correlation  Skewed 

Distinct71035
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.9228
Minimum0.4
Maximum2070111.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:35.753293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2
Q128.44
median163.2
Q3581.94
95-th percentile2639.32
Maximum2070111.4
Range2070111
Interquartile range (IQR)553.5

Descriptive statistics

Standard deviation73699.706
Coefficient of variation (CV)16.731214
Kurtosis590.18325
Mean4404.9228
Median Absolute Deviation (MAD)156.45
Skewness23.569078
Sum1.1315031 × 1010
Variance5.4316466 × 109
MonotonicityNot monotonic
2025-04-06T00:55:35.917644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62574
 
2.4%
2 60029
 
2.3%
3 44612
 
1.7%
4 38760
 
1.5%
5 28895
 
1.1%
6 25611
 
1.0%
7 18681
 
0.7%
8 17326
 
0.7%
9 13568
 
0.5%
10 13295
 
0.5%
Other values (71025) 2245373
87.4%
ValueCountFrequency (%)
0.4 6
 
< 0.1%
0.48 14
 
< 0.1%
0.52 5
 
< 0.1%
0.54 2
 
< 0.1%
0.8 4
 
< 0.1%
0.84 4
 
< 0.1%
0.96 11
 
< 0.1%
0.98 1
 
< 0.1%
1 62574
2.4%
1.02 1
 
< 0.1%
ValueCountFrequency (%)
2070111.4 1555
0.1%
1733209.4 1671
0.1%
1140865.5 94
 
< 0.1%
1021267.8 38
 
< 0.1%
952663.2 1735
0.1%
523610.44 1361
0.1%
504107 26
 
< 0.1%
442087.66 55
 
< 0.1%
336561 45
 
< 0.1%
331882.56 51
 
< 0.1%

total_gasto
Real number (ℝ)

High correlation 

Distinct93148
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3188.546
Minimum0.07
Maximum912072.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:36.052373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile10.849999
Q177.380005
median247.9
Q3636.31995
95-th percentile3023.46
Maximum912072.44
Range912072.37
Interquartile range (IQR)558.93994

Descriptive statistics

Standard deviation41173.977
Coefficient of variation (CV)12.913088
Kurtosis350.93192
Mean3188.546
Median Absolute Deviation (MAD)204.95
Skewness18.33987
Sum8.1904947 × 109
Variance1.6952963 × 109
MonotonicityNot monotonic
2025-04-06T00:55:36.239994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
812211.94 1735
 
0.1%
912072.44 1671
 
0.1%
757335.44 1555
 
0.1%
464574.28 1361
 
0.1%
573531.44 1318
 
0.1%
12543.24 1173
 
< 0.1%
1.13 1155
 
< 0.1%
19.88 1134
 
< 0.1%
18.47 1080
 
< 0.1%
1.2 1033
 
< 0.1%
Other values (93138) 2555509
99.5%
ValueCountFrequency (%)
0.07 2
 
< 0.1%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 3
< 0.1%
0.24 5
< 0.1%
0.25 1
 
< 0.1%
0.26 5
< 0.1%
0.27 5
< 0.1%
0.28 6
< 0.1%
ValueCountFrequency (%)
912072.44 1671
0.1%
812211.94 1735
0.1%
757335.44 1555
0.1%
573531.44 1318
0.1%
464574.28 1361
0.1%
396370.2 945
< 0.1%
247083.06 644
 
< 0.1%
186000.83 94
 
< 0.1%
149620.8 181
 
< 0.1%
129457.25 461
 
< 0.1%

precio_promedio
Real number (ℝ)

High correlation  Skewed 

Distinct260446
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4421912
Minimum0.0039547062
Maximum2177.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:36.512354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0039547062
5-th percentile0.38869548
Q10.6737593
median1.5778431
Q33.6011639
95-th percentile19.027935
Maximum2177.81
Range2177.806
Interquartile range (IQR)2.9274046

Descriptive statistics

Standard deviation10.496226
Coefficient of variation (CV)2.3628488
Kurtosis2438.0341
Mean4.4421912
Median Absolute Deviation (MAD)1.0798431
Skewness24.843452
Sum11410763
Variance110.17076
MonotonicityNot monotonic
2025-04-06T00:55:36.657936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85257 1735
 
0.1%
0.52623326 1671
 
0.1%
0.36584282 1555
 
0.1%
0.6 1415
 
0.1%
0.65 1378
 
0.1%
0.88725173 1361
 
0.1%
3.1338317 1318
 
0.1%
0.565 1300
 
0.1%
2.9663782 1173
 
< 0.1%
18.47 1142
 
< 0.1%
Other values (260436) 2554676
99.5%
ValueCountFrequency (%)
0.0039547062 1
 
< 0.1%
0.00685977 6
 
< 0.1%
0.0073786806 9
 
< 0.1%
0.007571423 7
 
< 0.1%
0.008853219 9
 
< 0.1%
0.009492378 13
 
< 0.1%
0.015799172 111
< 0.1%
0.016728375 17
 
< 0.1%
0.018946793 71
< 0.1%
0.024560008 33
 
< 0.1%
ValueCountFrequency (%)
2177.81 1
 
< 0.1%
1703.4 1
 
< 0.1%
1607.53 1
 
< 0.1%
1437.48 3
< 0.1%
1408.16 2
< 0.1%
1010.53 1
 
< 0.1%
849.62 1
 
< 0.1%
821.0675 4
< 0.1%
795.42 2
< 0.1%
795.01 1
 
< 0.1%

num_pedidos
Real number (ℝ)

High correlation 

Distinct128
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5446296
Minimum1
Maximum1133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:36.791721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile20
Maximum1133
Range1132
Interquartile range (IQR)4

Descriptive statistics

Standard deviation56.246915
Coefficient of variation (CV)5.8930433
Kurtosis276.17543
Mean9.5446296
Median Absolute Deviation (MAD)2
Skewness15.946754
Sum24517519
Variance3163.7154
MonotonicityNot monotonic
2025-04-06T00:55:36.930274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 610854
23.8%
2 464762
18.1%
3 334652
13.0%
4 241867
 
9.4%
5 177691
 
6.9%
6 130856
 
5.1%
7 100684
 
3.9%
8 76597
 
3.0%
9 59873
 
2.3%
10 48727
 
1.9%
Other values (118) 322161
12.5%
ValueCountFrequency (%)
1 610854
23.8%
2 464762
18.1%
3 334652
13.0%
4 241867
 
9.4%
5 177691
 
6.9%
6 130856
 
5.1%
7 100684
 
3.9%
8 76597
 
3.0%
9 59873
 
2.3%
10 48727
 
1.9%
ValueCountFrequency (%)
1133 1735
0.1%
1029 1671
0.1%
1015 1555
0.1%
860 1361
0.1%
562 1173
< 0.1%
559 945
< 0.1%
485 1318
0.1%
404 998
< 0.1%
348 644
 
< 0.1%
312 318
 
< 0.1%

ticket_promedio
Real number (ℝ)

High correlation 

Distinct131307
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.69413
Minimum0.07
Maximum18707.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:37.108653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile7.455
Q132.17575
median71.64333
Q3142.26875
95-th percentile382.50497
Maximum18707.72
Range18707.65
Interquartile range (IQR)110.093

Descriptive statistics

Standard deviation200.62754
Coefficient of variation (CV)1.6486213
Kurtosis604.72823
Mean121.69413
Median Absolute Deviation (MAD)47.34833
Skewness14.452625
Sum3.1259864 × 108
Variance40251.409
MonotonicityNot monotonic
2025-04-06T00:55:37.274565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
716.8684 1735
 
0.1%
886.3678 1671
 
0.1%
746.1433 1555
 
0.1%
540.20264 1361
 
0.1%
1182.5391 1318
 
0.1%
1.13 1257
 
< 0.1%
19.88 1184
 
< 0.1%
22.318933 1173
 
< 0.1%
1.2 1141
 
< 0.1%
18.47 1121
 
< 0.1%
Other values (131297) 2555208
99.5%
ValueCountFrequency (%)
0.07 2
 
< 0.1%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 3
< 0.1%
0.24 5
< 0.1%
0.25 1
 
< 0.1%
0.26 5
< 0.1%
0.27 5
< 0.1%
0.28 6
< 0.1%
ValueCountFrequency (%)
18707.72 2
 
< 0.1%
18029.578 6
 
< 0.1%
17066.71 5
 
< 0.1%
12910.56 2
 
< 0.1%
11729.96 3
 
< 0.1%
8857.183 94
< 0.1%
8598.75 13
 
< 0.1%
7946.05 24
 
< 0.1%
7676.9 1
 
< 0.1%
7607.127 3
 
< 0.1%

cantidad_promedio
Real number (ℝ)

High correlation  Skewed 

Distinct94247
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.55906
Minimum0.4
Maximum100145.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:37.408165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.6666666
Q110.28
median44.787777
Q3128.6355
95-th percentile435.4
Maximum100145.5
Range100145.1
Interquartile range (IQR)118.3555

Descriptive statistics

Standard deviation495.7702
Coefficient of variation (CV)4.0784305
Kurtosis8129.2788
Mean121.55906
Median Absolute Deviation (MAD)40.787777
Skewness73.019714
Sum3.1225168 × 108
Variance245788.09
MonotonicityNot monotonic
2025-04-06T00:55:37.624970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 79745
 
3.1%
2 66342
 
2.6%
3 41844
 
1.6%
4 30425
 
1.2%
5 20488
 
0.8%
6 16541
 
0.6%
1.5 14438
 
0.6%
2.5 10913
 
0.4%
7 9654
 
0.4%
8 8261
 
0.3%
Other values (94237) 2270073
88.4%
ValueCountFrequency (%)
0.4 8
< 0.1%
0.48 18
< 0.1%
0.52 5
 
< 0.1%
0.54 2
 
< 0.1%
0.6 2
 
< 0.1%
0.665 2
 
< 0.1%
0.7 2
 
< 0.1%
0.71999997 2
 
< 0.1%
0.74 4
 
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
100145.5 3
 
< 0.1%
54326.93 94
< 0.1%
42008.918 26
 
< 0.1%
40013 6
 
< 0.1%
31914.62 38
< 0.1%
18227.256 53
< 0.1%
17085.857 9
 
< 0.1%
17000 1
 
< 0.1%
16638 7
 
< 0.1%
15244.402 55
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.5 MiB
1
942232 
2
869680 
3
528945 
4
201365 
5
 
26502

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2568724
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Length

2025-04-06T00:55:37.758615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T00:55:37.875018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2568724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2568724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2568724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 942232
36.7%
2 869680
33.9%
3 528945
20.6%
4 201365
 
7.8%
5 26502
 
1.0%

Total de edificaciones en obra
Real number (ℝ)

Zeros 

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9911559
Minimum0
Maximum654
Zeros678206
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size19.6 MiB
2025-04-06T00:55:38.058357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile27
Maximum654
Range654
Interquartile range (IQR)6

Descriptive statistics

Standard deviation35.698612
Coefficient of variation (CV)3.9704141
Kurtosis124.03997
Mean8.9911559
Median Absolute Deviation (MAD)3
Skewness10.876594
Sum23095798
Variance1274.3909
MonotonicityNot monotonic
2025-04-06T00:55:38.273730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 678206
26.4%
6 610549
23.8%
1 303926
11.8%
3 220156
 
8.6%
10 143655
 
5.6%
16 142501
 
5.5%
27 77243
 
3.0%
2 66825
 
2.6%
13 59062
 
2.3%
5 47566
 
1.9%
Other values (82) 219035
 
8.5%
ValueCountFrequency (%)
0 678206
26.4%
1 303926
11.8%
2 66825
 
2.6%
3 220156
 
8.6%
4 35704
 
1.4%
5 47566
 
1.9%
6 610549
23.8%
7 39016
 
1.5%
8 6103
 
0.2%
9 18646
 
0.7%
ValueCountFrequency (%)
654 69
 
< 0.1%
451 395
 
< 0.1%
425 16697
0.7%
367 37
 
< 0.1%
299 70
 
< 0.1%
251 2
 
< 0.1%
223 6
 
< 0.1%
215 238
 
< 0.1%
202 162
 
< 0.1%
190 558
 
< 0.1%

Interactions

2025-04-06T00:54:44.829041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:05.157662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:17.082780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:29.626595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:42.374535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:55.079093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:07.037439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:18.976189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:32.296636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:45.003225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:57.689184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:10.829414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:22.971468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:36.425952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:50.243505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:03.948649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:17.221201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:30.192580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:42.060725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:54.810470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:06.991850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:19.128348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:32.193966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:45.272646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:05.716770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:17.557745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:30.206593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:42.935866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:55.727235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:07.560576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:19.531299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:32.775005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:45.409070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:58.308901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:11.343438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:23.576044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:36.927843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:50.880747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:04.491602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:17.775616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:30.661985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:42.679882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:55.289582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:07.541525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:19.744072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:32.680182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:45.824992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:06.241409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:18.109227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:30.728061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:43.469131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:56.305398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:08.119765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:20.046967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:33.371775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:45.793146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:58.969821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:11.881777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:24.139943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:37.660580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:51.573427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:04.958559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:18.246971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:31.141496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:43.260555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:55.814796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:08.092704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:20.309273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:33.144372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:46.193407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:06.672973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:18.595660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:31.228173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:43.945688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:56.721214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:08.712883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:20.648576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:33.878738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:46.295543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:59.389611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:12.410986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:24.711348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:38.293440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:52.203693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:05.527308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:18.711112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:31.632822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:43.645428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:56.426293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:08.700142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:20.849725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:33.710246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:46.723999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:07.412264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:19.125946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:31.709962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:44.497852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:57.124253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:09.126230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:21.182151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:34.442463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:46.822853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:59.841861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:12.998915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:25.226252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:38.740432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:52.647732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:06.141763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:19.209599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:32.042627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:44.177731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:56.946825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:09.167694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:21.355202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:34.251756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:47.249640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:07.846259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:19.522572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:32.323262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:44.971909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:57.627620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:09.717216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:21.782719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:35.038315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:47.375373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:00.557863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:13.375564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:25.844283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:39.360697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:53.092709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:06.679850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:19.733260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:32.489474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:44.626945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:57.400597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:09.694730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:21.979500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:34.794220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:47.720786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:08.279715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:20.009398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:32.964527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:45.508530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:58.112378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:10.256210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:22.277396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:35.586709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:47.856848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:01.195983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:13.770552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:26.517653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:40.042057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:53.657253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:07.257842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:20.309463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:33.024831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:45.158923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:57.843230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:10.179361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:22.561777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:35.313718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:48.258349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:08.810438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:20.557388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:33.561327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:46.021044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:58.644097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:10.726630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:22.790026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:36.147157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-04-06T00:50:38.743408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:51.025842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:03.455376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:15.328388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:28.060834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:41.037375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:53.807787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:06.578592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:19.007590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:32.535855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:45.997684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:59.847860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:13.158485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:26.043581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:38.382529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:50.950894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:03.265486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:15.376130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:28.301416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:41.155383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:53.665115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:14.053503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:26.232320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:39.276097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:51.653407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:03.925675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:15.891812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:28.686258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:41.524486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:54.398536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:07.198961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:19.541764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:33.076419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:46.660753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:00.376489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:13.638439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:26.605301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:38.875648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:51.458675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:03.769311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:15.869814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:28.847653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:41.778659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:54.060270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:14.590978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:26.844024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:39.913640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:52.186565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:04.526193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:16.429922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:29.095932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:42.063267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:54.876203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:07.755400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:20.125068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:33.702229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:47.170086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:00.929379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:14.296461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:27.193932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:39.473608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:52.042432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:04.256479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:16.412609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:29.403602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:42.284149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:54.615339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:15.012975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:27.408468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:40.386146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:52.741929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:05.048346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:16.917253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:29.661344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:42.657454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:55.325063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:08.388022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:20.779589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:34.141644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:47.773880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:01.596326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:14.910703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:27.891302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:40.031594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:52.575242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:04.707158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:16.859749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:29.907966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:42.764143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:55.258970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:15.413909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:27.965925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:40.862529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:53.257496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:05.572455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:17.356851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:30.307665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:43.226233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:55.927089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:08.901850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:21.313251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:34.740723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:48.427900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:02.072116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:15.483895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:28.489754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:40.496354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:53.182025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:05.173870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:17.356743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:30.361793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:43.370198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:55.759504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:15.944962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:28.522703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:41.332551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:53.918205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:06.044666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:17.844269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:31.142852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:43.756229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:56.562794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:09.555434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:21.929796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:35.285626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:49.085587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:02.691601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:16.041007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:29.143309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:41.044662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:53.737802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:05.795191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:17.959749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:30.978729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:43.763516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:56.280332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:16.586152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:29.092141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:41.839189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:50:54.409057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:06.504580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:18.439311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:31.609736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:44.257372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:51:57.093206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:10.313587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:22.447880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:35.934526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:52:49.648287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:03.262094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:16.643012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:29.659003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:41.511659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:53:54.236664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:06.378715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:18.600390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:31.657000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-06T00:54:44.181808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-06T00:55:38.606778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
GINIIPUGTotal de edificaciones en obraajustada_alineacionalineación con portafolio estratégicocantidadcantidad_promediocategoriacategoria_macrocategorias_diferentesclusteredadedad_promedioidingreso_laboral_promediolog_cantidadlog_preciolog_valornum_pedidospedidoporcentaje_urbanoprecioprecio_promedioticket_promediototal_gastototal_productosvalorzona
GINI1.000-0.466-0.103-0.040-0.040-0.014-0.0160.0290.0310.0200.093-0.022-0.3830.003-0.520-0.014-0.023-0.0430.015-0.004-0.321-0.023-0.041-0.069-0.036-0.006-0.0431.000
IPUG-0.4661.0000.1610.0200.0200.0050.0170.0480.0410.0480.1980.0430.5320.0020.8000.0050.0070.022-0.0190.0050.4420.0070.0290.0600.0310.0070.0221.000
Total de edificaciones en obra-0.1030.1611.000-0.000-0.000-0.004-0.0150.0070.0070.0090.035-0.001-0.2190.0060.118-0.0040.0010.001-0.018-0.0030.2860.0010.010-0.010-0.019-0.0200.0010.401
ajustada_alineacion-0.0400.020-0.0001.0001.0000.3660.1820.0250.0150.0060.048-0.0670.018-0.0590.0370.3660.4930.9820.093-0.0060.0130.4930.1010.3680.3070.1880.9820.005
alineación con portafolio estratégico-0.0400.020-0.0001.0001.0000.3660.1820.0260.0170.0050.053-0.0670.018-0.0590.0370.3660.4930.9820.093-0.0060.0130.4930.1010.3680.3070.1880.9820.004
cantidad-0.0140.005-0.0040.3660.3661.0000.4200.0360.0060.0030.064-0.0390.008-0.0570.0141.000-0.5300.3870.076-0.011-0.014-0.530-0.3900.2120.1870.3550.3870.006
cantidad_promedio-0.0160.017-0.0150.1820.1820.4201.0000.0480.0130.0160.085-0.0770.024-0.1200.0340.420-0.2340.1880.221-0.002-0.035-0.234-0.7250.6970.6020.8750.1880.034
categoria0.0290.0480.0070.0250.0260.0360.0481.0001.0000.1550.0860.0390.0420.0250.0350.2580.4740.2350.0580.0150.0400.0600.1200.0190.0330.0300.0170.031
categoria_macro0.0310.0410.0070.0150.0170.0060.0131.0001.0000.1260.0870.0260.0430.0210.0310.3220.3560.1010.0570.0180.0340.0430.0140.0110.0240.0180.0040.055
categorias_diferentes0.0200.0480.0090.0060.0050.0030.0160.1550.1261.0000.0890.0490.0490.1500.0340.0510.0780.0440.1060.0230.0490.0200.0060.0140.0870.0820.0080.070
cluster0.0930.1980.0350.0480.0530.0640.0850.0860.0870.0891.0000.0550.1810.0400.1180.0680.0610.0880.2030.0150.1500.0320.0100.0720.2260.2100.0550.244
edad-0.0220.043-0.001-0.067-0.067-0.039-0.0770.0390.0260.0490.0551.0000.0400.0570.025-0.039-0.029-0.068-0.080-0.0020.021-0.029-0.017-0.129-0.139-0.104-0.0680.089
edad_promedio-0.3830.532-0.2190.0180.0180.0080.0240.0420.0430.0490.1810.0401.000-0.0080.6160.0080.0060.0160.0150.0090.1410.0060.0070.0470.0440.0290.0161.000
id0.0030.0020.006-0.059-0.059-0.057-0.1200.0250.0210.1500.0400.057-0.0081.000-0.011-0.057-0.009-0.061-0.5050.5240.018-0.0090.051-0.108-0.386-0.336-0.0610.034
ingreso_laboral_promedio-0.5200.8000.1180.0370.0370.0140.0340.0350.0310.0340.1180.0250.616-0.0111.0000.0140.0160.040-0.0000.0060.5700.0160.0310.0870.0570.0270.0401.000
log_cantidad-0.0140.005-0.0040.3660.3661.0000.4200.2580.3220.0510.068-0.0390.008-0.0570.0141.000-0.5300.3870.076-0.011-0.014-0.530-0.3900.2120.1870.3550.3870.038
log_precio-0.0230.0070.0010.4930.493-0.530-0.2340.4740.3560.0780.061-0.0290.006-0.0090.016-0.5301.0000.4850.0290.0060.0191.0000.4320.0920.085-0.1620.4850.025
log_valor-0.0430.0220.0010.9820.9820.3870.1880.2350.1010.0440.088-0.0680.016-0.0610.0400.3870.4851.0000.091-0.0060.0090.4850.0860.3600.3000.1921.0000.064
num_pedidos0.015-0.019-0.0180.0930.0930.0760.2210.0580.0570.1060.203-0.0800.015-0.505-0.0000.0760.0290.0911.0000.016-0.0380.029-0.1100.1780.7210.6290.0910.180
pedido-0.0040.005-0.003-0.006-0.006-0.011-0.0020.0150.0180.0230.015-0.0020.0090.5240.006-0.0110.006-0.0060.0161.000-0.0010.0060.003-0.0000.0090.006-0.0060.014
porcentaje_urbano-0.3210.4420.2860.0130.013-0.014-0.0350.0400.0340.0490.1500.0210.1410.0180.570-0.0140.0190.009-0.038-0.0011.0000.0190.0490.007-0.017-0.0420.0091.000
precio-0.0230.0070.0010.4930.493-0.530-0.2340.0600.0430.0200.032-0.0290.006-0.0090.016-0.5301.0000.4850.0290.0060.0191.0000.4320.0920.085-0.1620.4850.000
precio_promedio-0.0410.0290.0100.1010.101-0.390-0.7250.1200.0140.0060.010-0.0170.0070.0510.031-0.3900.4320.086-0.1100.0030.0490.4321.000-0.066-0.104-0.6110.0860.005
ticket_promedio-0.0690.060-0.0100.3680.3680.2120.6970.0190.0110.0140.072-0.1290.047-0.1080.0870.2120.0920.3600.178-0.0000.0070.092-0.0661.0000.7800.6270.3600.042
total_gasto-0.0360.031-0.0190.3070.3070.1870.6020.0330.0240.0870.226-0.1390.044-0.3860.0570.1870.0850.3000.7210.009-0.0170.085-0.1040.7801.0000.8240.3000.197
total_productos-0.0060.007-0.0200.1880.1880.3550.8750.0300.0180.0820.210-0.1040.029-0.3360.0270.355-0.1620.1920.6290.006-0.042-0.162-0.6110.6270.8241.0000.1920.180
valor-0.0430.0220.0010.9820.9820.3870.1880.0170.0040.0080.055-0.0680.016-0.0610.0400.3870.4851.0000.091-0.0060.0090.4850.0860.3600.3000.1921.0000.027
zona1.0001.0000.4010.0050.0040.0060.0340.0310.0550.0700.2440.0891.0000.0341.0000.0380.0250.0640.1800.0141.0000.0000.0050.0420.1970.1800.0271.000

Missing values

2025-04-06T00:54:57.540686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-06T00:55:03.343759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-06T00:55:14.058378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechapedidoidedadmunicipiozonaasesorpunto de ventaclustercategoria_macrocategoriasubcategoriaproductocolorcantidadpreciovaloralineación con portafolio estratégicolog_cantidadlog_preciolog_valorajustada_alineacionedad_promedioingreso_laboral_promedioporcentaje_urbanoIPUGGINItotal_productostotal_gastoprecio_promedionum_pedidosticket_promediocantidad_promediocategorias_diferentesTotal de edificaciones en obra
01971-04-302252EL CARMEN DE CHUCURISANTANDERasesor_2punto_venta_2cluster_tienda_2categoria_macro_1categoria_2subcategoria_2producto_2GRIS1.0032.8832.882.9203200.6931473.5228253.522825-0.06177237.0770721.801097e+0686.881056184.00.50177.040000389.135.0510121232.42756.42000032.0
11971-04-303331VILLANUEVALA GUAJIRAasesor_3punto_venta_2cluster_tienda_2categoria_macro_2categoria_3subcategoria_3producto_3BEIGE2.000.561.130.1175041.0986120.4446860.756122-0.20114329.8626831.212095e+0684.79395366.00.5362.0000001.130.56500011.13002.00000011.0
21971-04-303331VILLANUEVALA GUAJIRAasesor_3punto_venta_2cluster_tienda_2categoria_macro_2categoria_3subcategoria_3producto_3BEIGE2.000.561.130.1175041.0986120.4446860.756122-0.20114329.8626831.212095e+0684.79395366.00.5362.0000001.130.56500011.13002.000000110.0
31971-04-303331VILLANUEVALA GUAJIRAasesor_3punto_venta_2cluster_tienda_2categoria_macro_2categoria_3subcategoria_3producto_3BEIGE2.000.561.130.1175041.0986120.4446860.756122-0.20114329.8626831.212095e+0684.79395366.00.5362.0000001.130.56500011.13002.000000116.0
41971-04-303331VILLANUEVALA GUAJIRAasesor_3punto_venta_2cluster_tienda_2categoria_macro_2categoria_3subcategoria_3producto_3BEIGE2.000.561.130.1175041.0986120.4446860.756122-0.20114329.8626831.212095e+0684.79395366.00.5362.0000001.130.56500011.13002.00000010.0
51971-04-304443VILLANUEVALA GUAJIRAasesor_4punto_venta_2cluster_tienda_2categoria_macro_3categoria_4subcategoria_4producto_4No encontrado1.008.388.381.2510720.6931472.2385802.238580-0.14569329.8626831.212095e+0684.79395366.00.5361.0000008.388.38000018.38001.00000011.0
61971-04-304443VILLANUEVALA GUAJIRAasesor_4punto_venta_2cluster_tienda_2categoria_macro_3categoria_4subcategoria_4producto_4No encontrado1.008.388.381.2510720.6931472.2385802.238580-0.14569329.8626831.212095e+0684.79395366.00.5361.0000008.388.38000018.38001.000000110.0
71971-04-304443VILLANUEVALA GUAJIRAasesor_4punto_venta_2cluster_tienda_2categoria_macro_3categoria_4subcategoria_4producto_4No encontrado1.008.388.381.2510720.6931472.2385802.238580-0.14569329.8626831.212095e+0684.79395366.00.5361.0000008.388.38000018.38001.000000116.0
81971-04-304443VILLANUEVALA GUAJIRAasesor_4punto_venta_2cluster_tienda_2categoria_macro_3categoria_4subcategoria_4producto_4No encontrado1.008.388.381.2510720.6931472.2385802.238580-0.14569329.8626831.212095e+0684.79395366.00.5361.0000008.388.38000018.38001.00000010.0
91971-04-305531VILLANUEVALA GUAJIRAasesor_5punto_venta_3cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_5BLANCO21.142.2747.993.7290243.0973861.1847903.891616-0.02047729.8626831.212095e+0684.79395366.00.53654.80999873.501.340996324.500018.26999921.0
fechapedidoidedadmunicipiozonaasesorpunto de ventaclustercategoria_macrocategoriasubcategoriaproductocolorcantidadpreciovaloralineación con portafolio estratégicolog_cantidadlog_preciolog_valorajustada_alineacionedad_promedioingreso_laboral_promedioporcentaje_urbanoIPUGGINItotal_productostotal_gastoprecio_promedionum_pedidosticket_promediocantidad_promediocategorias_diferentesTotal de edificaciones en obra
25687141972-09-0193393041922129CURITISANTANDERasesor_380punto_venta_10cluster_tienda_3categoria_macro_1categoria_1subcategoria_26producto_410No encontrado1.000.920.920.0829440.6931470.6523250.652325-0.20280337.0770721.801097e+0686.881056184.00.50141.24000093.650002.270853331.21666713.74666736.0
25687151972-09-0193393141922428CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_9subcategoria_25producto_267No encontrado1.0015.9015.902.3742720.6931472.8273142.827314-0.08944434.1939151.831206e+0683.56956065.00.4711.00000015.9000015.900000115.9000001.00000016.0
25687161972-09-0193393241922546CURITISANTANDERasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_9subcategoria_25producto_2690No encontrado1.0020.4620.463.3350400.6931473.0661913.066191-0.04063837.0770721.801097e+0686.881056184.00.5011.00000020.4600020.460000120.4600001.00000016.0
25687171972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_3200MULTICOLOR21.063.6777.308.0352003.0937661.5411594.3605480.20387837.0770721.801097e+0686.881056184.00.5011144.450000470.380000.411010858.797500143.05624026.0
25687181972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_699GRIS17.013.0451.725.3775362.8909271.3962453.9649950.06463937.0770721.801097e+0686.881056184.00.5011144.450000470.380000.411010858.797500143.05624026.0
25687191972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_7subcategoria_5producto_3328MULTICOLOR45.502.73124.0311.0211843.8394521.3164084.8285540.36252837.0770721.801097e+0686.881056184.00.5011144.450000470.380000.411010858.797500143.05624026.0
25687201972-09-019339347848929CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_4719BLANCO8.643.5630.782.8650242.2659211.5173233.458837-0.06458334.1939151.831206e+0683.56956065.00.471569.240000360.490020.633283572.098010113.84801026.0
25687211972-09-0193393541527942CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_10subcategoria_37producto_1414No encontrado1.0033.0433.043.7532160.6931473.5275363.527536-0.01923734.1939151.831206e+0683.56956065.00.4718.000000100.3000012.537500333.4333342.66666716.0
25687221972-09-0193393641922647NATAGAIMATOLIMAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_511MARFIL11.523.7543.144.4858882.5273271.5581453.7873660.01846038.6457371.550361e+0680.70770458.00.51813.12000148.230003.676067148.23000013.12000110.0
25687231972-09-0193393641922647NATAGAIMATOLIMAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_7subcategoria_5producto_248No encontrado1.603.185.090.4527360.9555111.4303111.806648-0.18493038.6457371.550361e+0680.70770458.00.51813.12000148.230003.676067148.23000013.12000110.0

Duplicate rows

Most frequently occurring

fechapedidoidedadmunicipiozonaasesorpunto de ventaclustercategoria_macrocategoriasubcategoriaproductocolorcantidadpreciovaloralineación con portafolio estratégicolog_cantidadlog_preciolog_valorajustada_alineacionedad_promedioingreso_laboral_promedioporcentaje_urbanoIPUGGINItotal_productostotal_gastoprecio_promedionum_pedidosticket_promediocantidad_promediocategorias_diferentesTotal de edificaciones en obra# duplicates
01971-01-0225524314829750ARGELIACAUCAasesor_138punto_venta_21cluster_tienda_3categoria_macro_2categoria_3subcategoria_3producto_40No encontrado2.000.561.130.1416961.0986120.4446860.756122-0.19998036.847561.417426e+0683.79595819.00.512509.88903.4900001.7719661464.53499636.42000040.02
11971-01-0225524314829750ARGELIACAUCAasesor_138punto_venta_21cluster_tienda_3categoria_macro_2categoria_3subcategoria_9producto_72GRIS150.000.1319.842.4952325.0172800.1222183.036874-0.08333036.847561.417426e+0683.79595819.00.512509.88903.4900001.7719661464.53499636.42000040.02
21971-01-0225524314829750ARGELIACAUCAasesor_138punto_venta_21cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_919MARFIL17.554.0470.968.4499202.9204701.6174064.2761100.22578836.847561.417426e+0683.79595819.00.512509.88903.4900001.7719661464.53499636.42000040.02
31971-01-0225524314829750ARGELIACAUCAasesor_138punto_venta_21cluster_tienda_3categoria_macro_4categoria_12subcategoria_14producto_221No encontrado1.0043.9143.914.5653760.6931473.8046613.8046610.02256436.847561.417426e+0683.79595819.00.512509.88903.4900001.7719661464.53499636.42000040.02
41971-01-0225653915024939ARGELIACAUCAasesor_93punto_venta_22cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_423BLANCO24.304.13100.2711.9370243.2308041.6351064.6177900.41156536.847561.417426e+0683.79595819.00.512151.65117.2700000.773294339.09000050.55000020.02
51971-01-0225691015991043ARGELIACAUCAasesor_111punto_venta_21cluster_tienda_3categoria_macro_4categoria_12subcategoria_14producto_221No encontrado1.0043.9143.914.5653760.6931473.8046613.8046610.02256436.847561.417426e+0683.79595819.00.5124.0090.59000022.647500330.1966651.33333330.02
61971-01-0225692615991043ARGELIACAUCAasesor_111punto_venta_21cluster_tienda_3categoria_macro_2categoria_3subcategoria_3producto_112GRIS2.000.561.130.1416961.0986120.4446860.756122-0.19998036.847561.417426e+0683.79595819.00.5124.0090.59000022.647500330.1966651.33333330.02
71971-01-0225720216009353ARGELIACAUCAasesor_138punto_venta_21cluster_tienda_3categoria_macro_2categoria_3subcategoria_9producto_170GRIS75.000.4433.314.8660484.3307330.3646433.5354370.03811136.847561.417426e+0683.79595819.00.51276.0048.9700000.644342224.48500038.00000010.02
81971-01-0225733716016638ARGELIACAUCAasesor_91punto_venta_21cluster_tienda_3categoria_macro_4categoria_10subcategoria_37producto_1823No encontrado1.0020.2220.221.7971200.6931473.0549443.054944-0.11847436.847561.417426e+0683.79595819.00.5122.0028.44999914.224999128.4499992.00000010.02
91971-01-0225733716016638ARGELIACAUCAasesor_91punto_venta_21cluster_tienda_3categoria_macro_4categoria_9subcategoria_10producto_1218No encontrado1.008.238.230.3214080.6931472.2224592.222459-0.19130436.847561.417426e+0683.79595819.00.5122.0028.44999914.224999128.4499992.00000010.02